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GroupBy.nth(n, dropna=None)
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Take the nth row from each group if n is an int, or a subset of rows if n is a list of ints.
If dropna, will take the nth non-null row, dropna is either Truthy (if a Series) or ?all?, ?any? (if a DataFrame); this is equivalent to calling dropna(how=dropna) before the groupby.
Parameters: n : int or list of ints
a single nth value for the row or a list of nth values
dropna : None or str, optional
apply the specified dropna operation before counting which row is the nth row. Needs to be None, ?any? or ?all?
Examples
>>> df = pd.DataFrame({'A': [1, 1, 2, 1, 2], ... 'B': [np.nan, 2, 3, 4, 5]}, columns=['A', 'B']) >>> g = df.groupby('A') >>> g.nth(0) B A 1 NaN 2 3.0 >>> g.nth(1) B A 1 2.0 2 5.0 >>> g.nth(-1) B A 1 4.0 2 5.0 >>> g.nth([0, 1]) B A 1 NaN 1 2.0 2 3.0 2 5.0
Specifying
dropna
allows count ignoring NaN>>> g.nth(0, dropna='any') B A 1 2.0 2 3.0
NaNs denote group exhausted when using dropna
>>> g.nth(3, dropna='any') B A 1 NaN 2 NaN
Specifying
as_index=False
ingroupby
keeps the original index.>>> df.groupby('A', as_index=False).nth(1) A B 1 1 2.0 4 2 5.0
GroupBy.nth()
2017-01-12 04:48:28
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